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‎13. Graphs/03. LeetCode Medium.ipynb‎

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" print(\"asdf\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 9. 1584. Min Cost to Connect All Points"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 63,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Solution:\n",
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" def minCostConnectPoints(self, points: List[List[int]]) -> int:\n",
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" d, res = {(x, y): float('inf') if i else 0 for i, (x, y) in enumerate(points)}, 0\n",
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" #print(d)\n",
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" #print()\n",
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" while d:\n",
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" x, y = min(d, key=d.get) # obtain the current minimum edge\n",
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" res += d.pop((x, y)) # and remove the corresponding point\n",
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" for x1, y1 in d: # for the rest of the points, update the minimum manhattan distance\n",
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" d[(x1, y1)] = min(d[(x1, y1)], abs(x-x1)+abs(y-y1))\n",
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" #print(x,y,d,res)\n",
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" #rint()\n",
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" return res"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"20"
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]
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},
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"execution_count": 64,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"points = [[0,0],[2,2],[3,10],[5,2],[7,0]]\n",
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"#points = [[3,12],[-2,5],[-4,1]]\n",
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"Solution().minCostConnectPoints(points)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 10. [547]. Number of Provinces"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 135,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Solution:\n",
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" def findCircleNum(self, isConnected: List[List[int]]) -> int:\n",
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" N = len(isConnected)\n",
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" seen = set()\n",
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" def dfs(node):\n",
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" for nei, adj in enumerate(isConnected[node]):\n",
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" if adj and nei not in seen:\n",
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" seen.add(nei)\n",
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" dfs(nei)\n",
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" \n",
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" ans = 0\n",
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" for i in range(N):\n",
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" if i not in seen:\n",
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" dfs(i)\n",
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" ans += 1\n",
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" return ans"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 136,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1"
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]
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},
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"execution_count": 136,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"isConnected = [[1,1,0],[1,1,0],[0,0,1]]\n",
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"isConnected = [[1,0,0],[0,1,0],[0,0,1]]\n",
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"isConnected = [[1,1,1],[1,1,1],[1,1,1]]\n",
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"Solution().findCircleNum(isConnected)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 11."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 12."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 13."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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{
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"cell_type": "code",
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": []
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"cell_type": "code",
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"execution_count": null,
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"source": []
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"cell_type": "code",
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"execution_count": null,
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"source": []
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{
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -556,7 +833,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.8 ('base')",
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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"version": "3.9.13"
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"hash": "fecfa28de78ead09ec2989a580469213059448baba5e0b02aeeb7641afb84221"
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}
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}
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},

‎16. Greedy Method/01. Prim's McST.ipynb‎

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"obj.printMst()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'List' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16976\\4071062386.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mclass\u001b[0m \u001b[0mSolution\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mminCostConnectPoints\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'inf'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# obtain the current minimum edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_16976\\4071062386.py\u001b[0m in \u001b[0;36mSolution\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mSolution\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mminCostConnectPoints\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpoints\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'inf'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# obtain the current minimum edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mNameError\u001b[0m: name 'List' is not defined"
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]
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}
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],
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"source": [
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"class Solution:\n",
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" def minCostConnectPoints(self, points: List[List[int]]) -> int:\n",
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" d, res = {(x, y): float('inf') if i else 0 for i, (x, y) in enumerate(points)}, 0\n",
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" while d:\n",
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" x, y = min(d, key=d.get) # obtain the current minimum edge\n",
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" res += d.pop((x, y)) # and remove the corresponding point\n",
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" for x1, y1 in d: # for the rest of the points, update the minimum manhattan distance\n",
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" d[(x1, y1)] = min(d[(x1, y1)], abs(x-x1)+abs(y-y1))\n",
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" return res"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -143,7 +173,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.8 ('base')",
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
@@ -157,12 +187,12 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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"version": "3.9.13"
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"vscode": {
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"hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
195+
"hash": "fecfa28de78ead09ec2989a580469213059448baba5e0b02aeeb7641afb84221"
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}
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}
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},

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